Abstract:

This dissertation deals with improving the reliability of evolutionary computation algorithms and accelerating problem-solving in optimization problems. Evolutionary algorithms have proven their value in difficult optimization problems that are not usually solvable in decent time using conventional optimization methods. However, evolutionary computation methods still suffer from problems related especially to premature convergence and the lengthy run times of the algorithms. In addition, the field of evolutionary computation does not commonly use the widely accepted practices for the comprehensive statistical comparison of two different evolutionary algorithms.

This dissertation aims at improving the process of using evolutionary computation in complex optimization problems from three perspectives. First, new algorithms are proposed for demanding optimization tasks. These algorithms rely on two perspectives, using a new multipopulation approach to enable appropriate conditions for candidate solutions to evolve and fusing evolutionary algorithms with other soft computing technologies, such as fuzzy logic, in a new way. Second, this dissertation discusses a method for reducing the computational time taken to evaluate a computationally demanding objective function value using neural network-based approximations. Third, a statistical method for comparing the results produced by two different evolutionary algorithms is illustrated. This method, relying on bootstrap resampling-based multiple hypothesis testing, is known outside the field of evolutionary computation, but has not been used within the evolutionary computing community. This dissertation illustrates the use of the statistical scheme and studies the parameters affecting the interpretation of its results.

The improvements to evolutionary algorithms this dissertation proposes have been proven to be beneficial by extensive testing. The proposed algorithms and the means to reduce the time required by the objective function evaluation have shown an increase in performance when compared to the reference algorithms. This dissertation also aims at awakening discussion related to the proper use of statistics in the field of evolutionary computation.Tämä väitöskirja käsittelee evoluutioalgoritmien luotettavuuden parantamista ja ongelmanratkaisun nopeuttamista optimointiongelmissa. Evoluutioalgoritmeja on käytetty menestyksekkäästi vaikeissa optimointiongelmissa, joita ei yleensä pystytä ratkaisemaan perinteisillä menetelmillä kohtuullisessa ajassa. Evoluutioalgoritmeilla on kuitenkin heikkouksia liittyen erityisesti ennenaikaiseen konvergoitumiseen ja algoritmien pitkiin suoritusaikoihin. Lisäksi evoluutiolaskennan alalla ei juurikaan käytetä yleisesti hyväksyttyjä menetelmiä kahden evoluutioalgoritmin perusteelliseen tilastolliseen vertailuun.